Method and apparatus for classifying a seed as inbred or hybrid

ABSTRACT

A apparatus for classifying a seed as inbred or hybrid comprises a terahertz signal source for emitting a terahertz signal towards the seed, a detector for detecting at least part of the terahertz signal having interacted with the seed, a scanner for moving the support relative to the terahertz signal to provide a scan of the seed, a data processing device for forming an image data from the detected terahertz signal as obtained for a plurality of positions during the scan of the seed, and a decision support system for providing a classification from the image data. In an embodiment, the terahertz signal source is arranged for emitting a continuous or pulse wave signal, and wherein the detector is arranged for detecting an amplitude and a phase of the terahertz signal having interacted with the seed. A signal representing an outcome of the classification may control a separator to separate seeds according to their classification.

The invention relates to an apparatus and a method for classifying aseed as inbred or hybrid. Furthermore, the invention relates to a use ofa terahertz system and to a seed selection system.

BACKGROUND

In crop production, high yields are desired so as to obtain a largeamount of crop per acre of land. Thereby, many factors, such as climate,composition of the soil, nutrition, etc. play a role. A quality of theseed from which the crop is grown, plays a relevant factor also, asgenetic properties of the plant have a substantial impact on the qualityand quantity of crop grow, as well as on susceptibility for diseases,hardness to withstand a certain climate, etc. A relevant factor is agenetic purity of the seed so as to be able to maintain desired geneticproperties of the seed.

It is known that certain crops, such as rice are self-pollinated. Selfpollination may result in seeds with low genetic diversity. The lowgenetic diversity in turn may lead to segregation of traits, loweryields and genetic deterioration of varieties.

Thus, there is a strong need for a distinguishing of a genetic purity ofa seed, in order to enable to quickly and precisely select hybrid seedsfrom inbred seeds, for example to provide a selection of high levelgenetic purity hybrid seeds to farmers for commercial cultivation.Estimating the genotypic difference of a seed has been performed invarious ways. These ways include destructive methods, such asmorphological, biochemical, DNA markers (DNA fingerprinting) methods:

-   -   morphological markers are influenced by the environmental        conditions, are labor intensive and time consuming;    -   biochemical markers such as isozyme and protein patterns are        least influenced by the environment however exhibit limited        polymorphism and often do not allow discrimination between        closely related inbred lines;    -   DNA markers (e.g. SSR markers) are still time consuming.

Furthermore, some non-destructive methods are known. An example ismaking use of near infrared radiation.

-   -   The seed is illuminated by light of a pre-defined NIR spectrum        and the spectral features of the reflected/transmitted light are        measured.    -   NIR reflection spectroscopy is an important technique that is        widely used for the analysis of grain and seed samples. The        spectral information obtained for the seeds samples under        investigation can be further examined to provide quantitative        information on vital composition parameters, such as protein,        carbohydrate, fat, moisture and sugar content by means of        chemometrics and appropriate calibration models.

NIR is a secondary technique, and therefore an accuracy of NIRinstruments depends on an accuracy of calibration as well as on asimilarity of a composition of a calibration sample to a sample to beanalyzed. Furthermore, variations in environmental factors, such ashumidity and temperature, may have an effect on the spectral informationobtained from the sample, thereby potentially influencing an accuracy ofthe NIR technique. In addition, NIR radiation penetrates only into asurface layer of the seed tissue which limits the body and amount ofgenetic material to be sampled.

SUMMARY OF THE INVENTION

The invention intends to provide an alternative for classifying theseed.

In order to achieve this goal, according to an aspect of the inventionthere is provided an apparatus for classifying a seed as inbred orhybrid, comprising:

-   -   a terahertz signal source for generating a terahertz signal,    -   a support for holding the seed,    -   a detector for detecting at least part of the terahertz signal        having interacted with the seed, the detector comprising a        detector output and being arranged for generating a detector        output signal at the detector output based on the detected at        least part of the terahertz signal,    -   a data processing device for forming an image data from the        detector output signal, and    -   a decision support system for providing from the image data a        classification of the seed as inbred or hybrid.

As compared to known techniques, the THz signal may penetrate deeperinto the sample, thus providing information about a composition of thesample from deeper into the sample instead of from a more superficialarea only. Because of the deeper penetration into the sample, moreinformation may be obtained from the sample.

Further, the THz signal may provide for spectral data on the one hand aswell as an image of amplitude information and phase information on theother hand. The spectral data as well as the amplitude and phaseinformation may be used. As dielectric (phase) contrast mechanismsindicating dielectric properties of the material under investigation arestrong at THz frequencies, hidden patterns in the seed may be revealedmore reliably. Still further, generally, THz radiation penetratesthrough and interacts with non-conductive and non-polar materials, whilebeing sensitive to water, potassium, phosphates, sugars, amino-acids,proteins etc.

Proteins are basics constituents of all living organisms and composed oforganic molecules, called amino acids which are joined covalently bypeptide bonds. The DNA contains the genetic information that dictatesthe specific sequence of amino acids. The relative composition ofprotein, oil, and starch in the seed kernel has a large geneticcomponent. Predictions of kernel composition using THz radiation, e.g.based on single-kernel THz spectroscopy or THz imaging may enable rapidselection of individual seed with desired traits.

Measurement of intensity of absorption, transmission and reflection ofTHz radiation (amplitude) and/or measurement of THz signal delay (phase)provides information about a condition of the seed kernel, as substancesthat are related to genetic properties of the seed (e.g. amino acids)interact with the terahertz radiation, which may tend to enable toobtain information substantially exactly about the aspects of the seedthat may be relevant for classifying the seed as hybrid or inbred, whilevarious substances in the seed that are less relevant for estimation ofthe inbreed/hybrid features, may tend to interact with the terahertzradiation in a different way. An image data is formed from the detectoroutput system, the image data may be a spectral image of the seed, a 2dimensional image of the seed (e.g. derived from amplitude and/or phasemeasurements) e.g. for a particular frequency or for a set offrequencies or a 3 dimensional image (e.g. derived from amplitude and/orphase measurements), whereby depth information is included in the image.The decision support system derives a classification from the imagedata. As the image data is based on THz radiation, substances that arerelevant to the classification of the seed (such as amino acids) have asubstantial effect on the image, thus being able to classify the seed ina reliable way. The decision support system may be formed by suitablesoftware, examples of which will be explained in more detail below.

In this document, the term terahertz (also abbreviated as THz) may beunderstood as a frequency range of 10 GHz-10,000 GHz, i.e. 0.01 THz to10 THz.

The terahertz signal source may comprise a single signal generator or anassembly of generator(s), mixer(s), pulse source(s), a continuous wavesource, etc. that together result in the generation of a terahertzsignal that is emitted to form a terahertz signal interacting with theseed.

The terahertz signal as generated by the terahertz signal source may beany signal type, such as an electrical signal conducted by an electricalconductor or waveguide, or an electromagnetic field, e.g. a near fieldor a far field type.

A coupling of the terahertz signal with the seed may be any type ofcoupling, such as an electromagnetic field coupling, e.g. near field offar field type. The signal as detected by the detector may be atransmission and/or a reflection of the terahertz signal havinginteracted with the seed.

The detector may comprise a detector-unit (comprising e.g. a lens and aterahertz receiver, an antenna and a terahertz receiver or the like) anda detection circuit, e.g. comprising one or more mixers, delay lines,synchronous detectors, filters, amplifiers, etc. in order to derive thedetection signal. The signal source and detector may in some embodimentsin part be integrated: for example, when deriving phase information fromthe detected terahertz radiation, the detection circuit of the detectormay make use of a reference signal obtained from the terahertz signalgenerator.

The signal source and detector may make use of components operating atroom temperature. Also, use may be made of cooled components or circuitparts, e.g. using cryogenic cooling.

In order to obtain an image (i.e. a data set that e.g. represents an atleast 2 dimensional representation of the measurement data obtained bythe detector), several approaches are possible, as will be brieflydescribed below.

Firstly, use may be made of a plurality of terahertz signals. Thereby,use may be made of a plurality of signal sources, a plurality ofdetectors or both. As a result, a plurality of detections may beperformed, e.g. one per detector, so as to obtain a correspondingplurality of data points, each representing a measurement at aparticular spot of the seed. The signals (and correspondingly, the spotsof the seed that are measured) may be arranged in a form of a line (aone dimensional matrix) or in a form of a two dimensional matrix. In thecase of a one dimensional matrix, a scanning movement of the seed may beused to complement the one dimensional matrix of detection towards a twodimensional one (the scanning e.g. in a direction perpendicular to theline along which the spots on the seed are located where the signalsinteract with the seed). The plurality of emitted terahertz signals maybe generated each by their own circuit, however it is also possible thatuse is made of one or more splitters to spit a single signal from asingle signal source into plural ones. Secondly, use may be made of ascanner. Thereby, the apparatus may comprise a scanner for moving thesupport relative to the terahertz signal to provide a scan of the seed,the data processing device being arranged forming an image data from thedetector output signal as obtained for a plurality of positions duringthe scan of the seed. In order to obtain an image, the scanner isarranged to perform a scanning movement whereby the terahertz signal(e.g. a beam) is moved in respect of the seed or vice versa. The scannermay thereto move the support, the emitted terahertz signal beam or both.The emitted terahertz signal beam may be moved by any suitable means,such as moving a coupling part of the signal source and/or detector,etc. The movement may be formed by a movement in at least 2 dimensions,for example scanning a plane substantially perpendicular to propagationdirection of the THz radiation towards the seed. Depth information maybe added by further including a scanning in a direction parallel to thepropagation direction of the THz radiation. The scanning movement may inaddition to the above described movements or instead thereof alsocomprise a rotation, e.g. along 2 or 3 rotational axes so as to obtainat least partly circumferential image data of the seed to be tested,allowing to test geometrically complex forms.

During performing the scan, the detector successively detects at leastpart of the terahertz radiation having interacted with the seed, for thedifferent scanning positions and/or scanning angles. During thescanning, the source may generate the terahertz radiation continuouslywhich may provide a fast processing, as the measurement may be performedduring the scanning movement. Alternatively, the scanner maysuccessively provide stationary scanning positions in a sequence, whichmay provide for more accurate measurements (hence a higher image qualityand estimation), possibly at a somewhat longer processing time.

As already indicated above, a combination of scanning and a plurality ofemitted terahertz-signals may be provided, e.g. in the example of a onedimensional matrix of signals, combined with a scanning in perpendiculardirection. Another example is a two dimensional matrix of signals,supplemented by a scanning in order to increase a resolution, i.e.increase a number of data points of the image data by scanning in aspatial range between the dots of the two dimensional matrix. A stillfurther example is the combination of a single signal source and singledetector with a one dimensional scanner which provides a scanningmovement along a single direction. The single detector in combinationwith the one dimensional scanner movement provides for a line typeimage, comprising a continuous signal or a plural of pixels representinga line type image. In particular in case the scanner is formed by aconveyor that feeds the seed into or through the apparatus, a fast (nofurther scanning), reliable (giving a line image that allows a betterestimation then would have been possible with a single measurement only)and low cost estimation.

The data processing device forms an image from the detector outputsignal. A variety of techniques may be used.

In an embodiment, the image data forms a single pixel (i.e. the imagedata being formed by a single value), the data processing device therebyforming a single pixel image data, for example using amplitude of thedetection signal, phase of the detection signal or a combinationthereof. Thereby, a fast determination may be provided, which may besufficient to for example recognize an empty seed. Such a single pixeldetermination may also be used as a pre-scan, i.e. in case the singlepixel determination provides that the seed is empty or otherwisestrongly affected, the process is stopped, while otherwise, a moredetailed image capturing is started to perform a more accurateclassification. Such a two step approach may make the classificationfaster, as obviously defect seeds may be recognized relatively fast. Inanother embodiment, multiple pixels (i.e. a detector signal at multiplespots of the seed) are captured by the data processing device. Thereto,use may be made of scanning as described above, multiple emittedterahertz signals as described above or both.

The image data may hence comprise a single value, a 1 dimensionalpattern, a 2 dimensional pattern, a 3 dimensional pattern, the patternse.g. comprising a reflection pattern, an absorption pattern, a receivedsignal time pattern, etc.

In an embodiment, the data processing device is arranged to derive animage from the combined detector output and the position and/or angleinformation (as may e.g. be provided by the scanner or derived from amulti signal beam dimensioning) so as to build the image from acombination of position and detector data.

The data processing device and decision support system may beimplemented as software to be executed in a computing device, such as acomputer, microcontroller, distributed computer network, or any otherdata processing arrangement. The data processing device and decisionsupport system may be separate entities (e.g. separate softwareprograms, or even separate computing devices each being assigned a taskof data processing or decision support), however it is also possiblethat the data processing device and decision support system areintegrated, e.g. implemented as software processes running in a singlesoftware program. The decision support system may be provided locally,e.g. implemented by a computer which is on site where the measurementsare performed, however it is also possible that the decision support (orpart thereof) is located remotely, for example making use of a remotedatabase of decision rules, references, reference images, etc.

The decision support system may generally be implemented as comprising aset of rules and references, and being arranged to provide a possibleoutcome based on such set of rules and references. The references mayfor example comprise reference images, reference thresholds for certainparameters (such as size of the seed, size of area's defined in theimage in the seed which exhibit comply to a predefined criterion, etc.).The rules may hold that a seed having a measured property exceeding avalue of the corresponding threshold should be classified into at leastone of hybrid and inbred, etc. The rules may further provide comparisonrules, e.g. to assign a classification outcome to the seed based on thecomparison of the image data of the seed with the reference image data.The rule may for example assign to the seed a same classification as theclassification of the reference image data that appears (from thecomparison) to be most closest, i.e. most similar. As another example anaverage or weighted average may be taken of the classification of asubset of the reference image data of seeds that appear to be highlysimilar, etc.

The term classification as inbred or hybrid is to be understood as anassigning of a class to the seed, the class being selected from a groupcomprising inbred and hybrid. Further classes related to other genomicproperties of a seed may be provided too. The term seed is to beunderstood so as to comprise any seed. In an embodiment, the seed is aplant seed. The term plant seed is to be understood so as to includegrain seeds, vegetable seeds, flower seeds etc. Non limiting examples ofa plant seed may include maize seed, wheat, rice, asparagus, radicchiorosso, tomato seed, pepper seed, seed-onion, carrot seed, cucumber seed,etc. In an embodiment, the plant seed is a vegetable seed, flower seed,grain seed, etc.

The classification (and a corresponding signal) may be formed by adiscrete value, e.g. a digital value, e.g. a class: “hybrid” or“inbred”, etc. The term hybrid, also referred to as heterozygous, may beunderstood as referring to the production of offspring by crossingbetween two genetically dissimilar parents. The term inbred, alsoreferred to a as homozygous, may be understood as referring to theproduction of offspring from the mating or breeding of parents that areclosely related genetically or are genetically the same.

The apparatus according to the invention may further comprise aseparator. The separator may have a control input that receives a signalrepresentative of the classification of the seed as inbred or hybrid,and may separate the seeds accordingly. The separator may e.g. comprisea sorter or any other separation device. The separator, which may alsobe referred to as a selector, as described below, may perform aselection accordingly. In another embodiment, the classificationprovides for a value in a range, such as a numeric value, having a rangewhich for example expresses a likelihood that the seed belongs to acertain class, the value e.g. ranging from highly likely to be hybrid tohighly likely to be inbred. The separator may accordingly sort the seedsin different categories according to their likelihood of being inbred orhybrid, e.g. by sorting the seeds in seeds that are highly likely to beinbred, seeds that are highly likely to be inbred and remaining seeds.More refined sorting techniques in accordance with a classificationvalue may easily be envisaged.

The terahertz signal source may directly generate a signal in theterahertz frequency band. Alternatively, up conversion techniques,mixing, or other techniques may be used to convert an initial signal ata lower frequency band into a terahertz signal. Similarly, the detectormay immediately detect a terahertz band signal. Furthermore, downconversion techniques, mixing, or other techniques may be used toconvert down to a lower frequency band before detection or as a part ofthe detection. For example, up conversion from and down conversion tothe microwave frequency band may be applied, allowing to may use ofmicrowave equipment, for example for measuring amplitude and phase, e.g.using a microwave vector network analyzer. A coupler may be providedthat couples the signal as generated by the signal generator, to theseed. In addition, the THz signal frequency can be continuous, or sweptor the THz signal can be pulsed as, for instance in time domainreflectometer (TDR) or general time domain THz technique, or can beobtained as a difference of two photonic high frequency signals or canbe generated as harmonic of low frequency signal.

The support may comprise any suitable support to hold the seed, e.g. avacuum clamp, an electrostatic clamp, a table, a conveyor belt, etc.

In an embodiment, the terahertz signal source is arranged to emittingthe terahertz signal in a range of 0.01 to 10 THz (i.e. 10 GHz to 10000GHz). The signal source may be arranged to emit, during testing a seed,a single frequency to the seed. In an alternative embodiment, the signalsource may be arranged to emit a plurality of frequencies during testingthe seed, e.g. simultaneously or as a time series, e.g. as a frequencysweep, allowing to obtain depth information, enabling to derive by thedata processing device an image comprising depth information using asimplified (e.g. two dimensional) imaging, e.g. using scanning (i.e.scanning to perform imaging at different depths may be at leastpartially omitted). A plurality of frequencies (e.g. applying afrequency sweep or applying frequency steps, may also be applied toimprove a signal to noise ratio of the image data, as artifactsoccurring at a particular one of the frequencies, while being absent atother frequencies (or having another effect at other frequencies mayhave a reduced impact on the image data. Thereto, for example, the dataprocessing device may add or average the image data obtained at thedifferent frequencies, into a single image data, so as to reduce aneffect thereof. The frequency sweep may also be used to provide aspectroscopic information.

In an embodiment, the terahertz signal source is arranged for (e.g.continuously or repetitively) emitting a continuous wave signal, and/ora pulse signal. In an embodiment, the detector is arranged for detectingan amplitude of the terahertz signal having interacted with the seed,the detector output signal being representative of a detected amplitudeof the terahertz signal. Detecting amplitude, in an embodiment withoutdetecting phase, allows a relatively low cost setup, as a less complexsetup may be chosen whereby the comparison of the received signal to asignal derived from the transmitted signal (for reference purpose) inorder to derive phase information may be omitted. Amplitude detectionmay performed with the terahertz signal source (e.g. continuously orrepetitively) emitting a continuous wave signal, and/or a pulse signal.

In an embodiment, the terahertz signal source is arranged for (e.g.continuously or repetitively) emitting a continuous wave signal, and/ora pulse signal. In an embodiment, the detector is arranged for detectingan amplitude and a phase of the terahertz signal having interacted withthe seed, the detector output signal being representative of a detectedamplitude and phase of the terahertz signal. By detecting amplitude andphase of the signal having interacted with the seed,absorption/reflection on the one hand as well as e.g. dielectricproperties derived from phase information on the other hand may be takeninto account. A high contrast image data may be obtained, the image datacomprising a high information content of data relevant to theclassification, allowing to perform a reliable estimation. In order todetect amplitude and phase of the signal having interacted with theseed, use may be made of a Vector Network Analyzer that enables todetect amplitude and phase by comparison with a reference signalobtained from the signal source. Amplitude and phase detection mayperformed with the terahertz signal source (e.g. continuously orrepetitively) emitting a continuous wave signal, and/or a pulse signal.In another embodiment, the detector is arranged for detecting a phase ofthe terahertz signal having interacted with the seed, the detectoroutput signal being representative of a detected phase of the terahertzsignal. Detection of only phase may allow to image dielectric propertiesof the seed.

In an embodiment, the data processing device is arranged for combiningamplitude and phase data as comprised in the detector output signal, andfor forming an image data of the seed from the combined amplitude andphase data (as obtained during the scanning). The amplitude and phasedata may e.g. be added allowing to obtain a combined image data ofamplitude and phase information, thus including absorption/reflection onthe one hand as well as e.g. dielectric properties derived from phaseinformation on the other hand. A high contrast image data may beobtained, the image data comprising a high information content of datarelevant to the estimation of hybrid/inbreed features, allowing toperform a reliable estimation. Further examples of an image dataprovided by the data processing device may be an image data of anamplitude signal as obtained from the detector (expressing reflection,absorption, transmission or a combination thereof), an image data of aphase signal as obtained from the detector (expressing e.g. dielectricproperties of the materials in the seed), a set of both amplitude andphase image data. The image data may be a 1 dimensional image data, a 2dimensional image data or a 3 dimensional image data (also containingdepth information). Depth information may be obtained from a suitable 3dimensional scanning, phase information or by making use of pluralfrequencies (e.g. a frequency sweep or stepwise frequency changes, or atime pulse) so as to obtain depth information.

Furthermore, spectral information may be used. Thereto, in anembodiment, the terahertz signal source is configured for generating theterahertz signal at a plurality of frequencies, the detector beingconfigured to detect at least part of the terahertz signal havinginteracted with the seed at each of the plurality of frequencies, thedetector output signal comprising a spectral signal, the data processingdevice being configured for forming a spectral image from the detectoroutput signal as obtained from the seeds of the set of learning seeds.Thus, spectral information may be obtained by transmitting a pluralityof THz frequencies, either simultaneously, using a frequency sweep, ortransmitting different frequencies successively, and a response(absorption, reflection and/or phase) at each of the frequencies beingdetected by the detector. The detector output signal hence comprisesspectral information. Specific amino acids or other substances mayexhibit specific absorption and/or reflection at specific frequencies,enabling to recognize a presence and/or concentration of such substancefrom the spectral properties of the image data obtained. Proteins arebasics constituents of all living organisms and composed of organicmolecules, called amino acids which are joined covalently by peptidebonds. The DNA contains the genetic information that dictates thespecific sequence of amino acids. Examples of such amino acids may beisoleucine, glutamic acid, leucine, glycine, tyrosine, histidine andtheir combinations. Given the link between the presence andconcentration of such amino acids and the genetic structure of the seed,spectral data that provides information about a contents of suchsubstances (e.g. amino acids) may provide relevant information toclassify the seed with a high reliability and in different classes.

Spectral information may also be used as follows: In an embodiment, theTerahertz signal source emits pulses. A pulse in a terahertz frequencyrange is to be understood as a pulse in a nanosecond range, having apulse width in a range of e.g. 1 ns to 0.1 ps. A response (reflection ortransmission or both) of the pulse is detected by the detector. Areference path, such as a reference delay may in parallel guide thepulse to the detector. A combined signal is detected by the detector andmay be transformed into spectral information using mathematicaltechniques, such a s a Fourier transform, whereby a frequency content isderived from the detected pulse response. Furthermore, a THz signalhaving a time varying amplitude may also be used to interrogate thesample. Detected THz pulse may be used to obtain a spectral response byusing mathematical techniques e.g. Fourier Transform etc. Thus, spectralinformation may be obtained using a relatively low complexity signalsource that emits a pulse train instead of requiring frequency sweeps,multiple frequencies, etc.

Accordingly, using spectral information (by means of any of the abovedescribed techniques), the image may be comprise a spectral image. Thespectral image may be a single value, a 1 dimensional pattern, a 2dimensional pattern, a 3 dimensional pattern, etc.

The interaction of the signal with the seed may be transmission throughthe seed, reflection by the seed or a combination thereof. In anembodiment, the signal generator source and the detector are arrangedfor free space coupling, also referred to as quasi optical coupling. Thecoupler transmits by free space coupling the generated terahertz signalto the seed, and the detector detects by free space coupling the signalthat interacted with the seed. Using free space coupling, no physicalcontact needs to be made by signal source and/or detector, allowing toperform the scan relatively fast and reducing a risk of invoking anymechanical damage to the seed during the process. Likewise, in anotherembodiment, the signal generator source and the detector may be arrangedfor near field coupling with the seed.

Instead of or in addition to a continuous wave signal, use may be madeof a pulsed signal. Accordingly, in an embodiment, the terahertz signalsource is arranged for emitting a terahertz pulse signal. The pulsesignal may comprise a single pulse or a plurality of pulses, e.g. a timesequence of pulses. Accordingly, the terahertz signal may comprisesingle pulse or a plurality of pulses. In the context of pulses, theterm terahertz is to be understood as pulses that provide a frequencycontent (i.e. their frequency domain energy content being in or reachinginto the terahertz frequency band). In the case of pulses, the detectormay be arranged to detect a time response, such as a time domainreflection. Accordingly, in data processing device may comprise a timedomain reflectometer.

In an embodiment, the decision support system is arranged for comparingthe obtained image data of the seed with at least one reference imagedata stored by the decision support system, and deriving theclassification of the seed from the comparison. The reference image datamay comprise one or more of image data of inbred seeds, hybrid seeds,and other genomic conditions of seeds (the reference image data beinge.g. obtained from scanning reference examples of seeds). Thereby, theapparatus may easily be learned for different seed types and differentconditions, by measurement of sample(s) in various conditions, storingthe obtained image data of the reference sample(s) for comparison. Thereference image data may alternatively be pre-stored or remotelyaccessible, for example from a remote server connected to the decisionsupport system via the internet.

In the case of the terahertz signal source generating a pulse, thereference image pattern(s) may be reference time domain reflectionpattern(s). Different reference time domain reflection pattern(s) may beprovided representing various conditions of the seed. In the case of asingle pixel image, the reference image data may comprise a referencevalue. Different reference values may represent various genomicconditions.

The decision support system may be learned, an example being provided asfollows. First, a set of seeds are tested in order to estimate theirclass (inbred, hybrid), this may be done using another technique, suchas NIR. Each seed of the set is then assigned a classification (based onthe analysis by the other technique). The seeds are subjected to theterahertz testing as described in order to obtain image data for eachseed of the set. The obtained image data for each seed is coupled to theclassification as obtained by the other technique. The image data incombination with the estimate is then stored as reference image data.Another example of learning the decision support system may be to usingthe terahertz apparatus and/or method as described in this document forgeneration of image data for each seed of the set. Based on the imagedata, the classification is however performed by an operator, such as atrained operator. The obtained image data for each seed is coupled tothe classification as provided by the operator. The image data incombination with the classification is then stored as reference imagedata. Accordingly, in an embodiment, the apparatus is further configuredto operate in a learning mode, the decision support system in thelearning mode being configured to store image data as obtained fromseeds of a set of learning seeds, and to associate a givenclassification to the image data. The given classification can beprovided by an operator (who inspects the image data) or from anotherclassification technique, such as a destructive classificationtechnique. In an embodiment, the decision support system is furtherconfigured to in the learning mode derive a criterion for classificationof a seed from the image data of the set of learning seeds and theassociated given classification data, and to provide the classificationof a following seed not comprised in the set of learning seeds, usingthe criterion. Thus, a criterion may be learned e.g. from a correlationbetween an occurrence of certain characteristics in the image data andthe classification. The criterion may, for example, comprise at leastone of an absorption in a specified frequency band, a reflection in aspecified frequency band, to thereby enable to derive a classificationfrom spectroscopic information as described above.

Another embodiment for learning patterns from THz images, comprisesusing supervised machine leaning approach, where feature vectors basedon fft (fast fourier transform) or wavelet coefficients are constructedand trained using a machine learning algorithm, e.g. such as SVM(support vector machine). Pattern recognition techniques may be used toautomatically or semi-automatically inspect THz images. The patternrecognition techniques comprises several steps. First, a “corpus”, i.e.collection of labeled examples (feature vectors) derived from THzimages, is constructed. Second, the corpus is randomly split into trainand test sets (using e.g. a 90/10 split) where the train set will beused to train the classifier and the test set will be used to evaluatethe classifier performance. Mathematically spoken, during the trainingphase a classifier learns a separation hyperplane in feature space. As ameasure of classifier performance a (classical) micro-averaged Recall,Precision and F1-value are estimated. Within these training, testing andevaluating phases the classifier is actually built. Finally, theobtained classifier is used to predict the labels (classes) for unseenexamples. As a classification algorithm we use the Support VectorMachine (SVM). SVM is a popular classification algorithm that has beenused successfully in various applications. SVM was designed to find aunique, optimal separation hyperplane. A hyperplane is consideredoptimal when it separates the positive and the negative trainingexamples in such a way that it has the largest possible margin to thenearest training examples as presented. SVM basically solves a specialconvex Quadratic Programming problem, which is quite computationallydemanding, however, an accurate estimation may be achieved.

According to a further aspect of the invention, there is provided amethod for classifying a seed as inbred or hybrid, comprising:

-   -   generating a terahertz signal,    -   holding the seed by a support,    -   coupling the terahertz signal to the seed,    -   detecting at least part of the terahertz signal having        interacted with the seed and generating a detector output signal        based on the detected at least part of the terahertz signal,    -   forming an image data from the detector output signal, and    -   providing from the image data a classification of the seed as        inbred or hybrid. The decision support system may be applied to        provide the classification from the image data. The image data        may comprise a spectral image.

According to a still further aspect of the invention, there is provideduse of a terahertz system for classifying a seed as inbred or hybrid,the terahertz system comprising:

-   -   a terahertz signal source for generating a terahertz signal,    -   a support for holding the seed,    -   a detector for detecting at least part of the terahertz signal        having interacted with the seed, the detector comprising a        detector output and being arranged for generating a detector        output signal at the detector output based on the detected at        least part of the terahertz signal,    -   a data processing device for forming an image data from the        detector output signal. The decision support system may be        applied to provide the classification from the image data.

According to yet another embodiment of the invention, there is provideda selection system for selecting a seed, comprising:

-   -   an apparatus according to the invention, the apparatus further        comprising a seed classification output and being arranged for        providing a seed classification output signal at the seed        classification output, the seed classification output signal        being representative of a classification of the seed as inbred        or hybrid,    -   a feeder, upstream of the apparatus, for feeding a seed into the        apparatus,    -   a separator, downstream of the apparatus, the separator having a        control input being connected to the seed classification output        of the apparatus, the separator being arranged for directing the        seed to a first output of the separator in response to the seed        classification output signal having a first value and to a        second output of the separator in response to the seed        classification output signal having a second value. Thus,        automatic or semi-automatic selection of the seeds in accordance        with their classification may be performed: a threshold may be        applied (e.g. expressing a minimum requirement for        classification as one of inbred and hybrid) and seeds having an        classification outcome exceeding the threshold may be directed        to the first output while seeds having a classification outcome        below the threshold may be direct to the second output. The        selector may for example be pneumatic (directing the seed by an        air stream), electrostatic, mechanical or by any other suitable        means. The feeder may comprise any transport mechanism such as a        conveyor belt, a downwardly sloping chute, a pneumatic seed        propelling means, etc. The feeder may further comprise a        sequencing device that sequentially releases the seeds one after        the other, each to be fed to the apparatus for classification.

With the method, use and selection system according to aspects of theinvention, the same advantages and effects may be achieve as with theclassification system according to an aspect of the invention. Also, thesame or similar embodiments may be provided as with the classificationsystem according to an aspect of the invention, achieving the same orsimilar effects as similar embodiments of the classification systemaccording to the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Further advantages, features and effects of the invention will followfrom the enclosed drawing, showing a non-limiting embodiment of theinvention, wherein:

FIG. 1 depicts a general block schematic view of a system in accordancewith an embodiment of the invention;

FIG. 2 depicts a schematic view of a terahertz source and detector ofthe system in accordance with FIG. 1;

FIG. 3 depicts a schematic top view of a measurement arrangement toillustrate the source and detector as described with reference to FIG.2;

FIG. 4 depicts a block schematic view of a separation system inaccordance with an embodiment of the invention;

FIGS. 5A and 5B depict frequency diagrams based on which an aspect ofthe invention will be explained;

FIG. 5C depicts a frequency diagram based on which an aspect of theinvention will be explained;

FIG. 6 depicts a flow diagram based on which an aspect of the inventionwill be explained;

FIG. 7 depicts a schematic view of a system in accordance with anembodiment of the invention; and

FIG. 8 depicts a schematic view of a decision support system inaccordance with an aspect of the invention.

It is noted that throughout the figures the same or similar referencenumerals are applied to indicate the same of similar elements.

DETAILED DESCRIPTION

FIG. 1 depicts a block schematic view of a system in accordance with anembodiment of the invention. The system comprises a terahertz signalsource SRC that generates a terahertz signal THS, such as a continuouswave signal. Alternatively, the source generates a pulsed signal. Anoutput of the source carrying the terahertz signal is connected to acoupler (coupling device) CPL that couples the terahertz signal to theseed SD. The coupling device may comprise a combination of a horn and alens, such as a HDP (high density polyethylene) lens in order to directthe terahertz radiation as generated by the source towards the seed asrepresented by Terahertz signal beam TSB. The seed is held by a supportSUP, examples of which may include a table, a vacuum clamp, anelectrostatic clamp, etc. A detector DET of the system detects at leastpart of the terahertz signal having interacted with the seed. Although,in the schematic drawing in accordance with FIG. 1, the source anddetector are schematically depicted at different sides of the seed, thedetector may in reality for example be positioned so as to receive apart of the terahertz radiation that has been reflected by the seed or apart of the terahertz radiation as transmitted by the seed or acombination thereof. The detector in this example comprises a terahertzdetection device, such as a sub-harmonically pumped superlatticeelectronic device (SLED) and a detection circuit that generates adetector output signal from the output signal of the terahertz detectiondevice (the detection device and the detection circuit having beensymbolically indicated in FIG. 1 as separate entities together formingthe detector). The terahertz detection device may directly perform adown conversion so as to convert the detected terahertz signal into asignal at a lower frequency band. The detection circuit may generate asingle detector output signal DO or a plurality of detector outputsignals, e.g. one representing amplitude and one representing phase. Inorder for the detector to operate in synchronism with the terahertzsignal source, a synchronization signal may be provided by the source tothe detector (or vice versa), as indicated in FIG. 1 by the dotted line,e.g. allowing to perform a phase measurement by the detector. Thedetector output signal, which may represent amplitude, phase or both, isprovided to a data processing device DPD which generates an image dataID of the seed. Thereto, the seed is scanned by a scanner SC which maymove the terahertz signal in respect of the seed or vice versa, imagedata is formed whereby by the data processing device combines thedetector output signal as obtained for the different positions achievedduring the scanning. The image data may form a two dimensional imagedata, using a 2 dimensional scan. Also, 3 dimensional images may beprovided, either by providing a 3D scan, collecting phase information orby providing the signal source to emit a plurality of frequencies,whereby the data processing device is arranged for deriving the 3dimensional image data from the 3D scan, the detector response at thedifferent frequencies or both. The data processing device may furtherapply suitable processing techniques, such as filtering for noisereduction, averaging measurements obtained at different frequencies forimproving signal to noise ratio, etc. The image data is provided to adecision support system DSS, in order to provide a classification CLAinto one of the classes hybrid and inbred. As depicted in FIG. 8, thedecision support system performs a determination by comparing the imagedata ID of the seed to reference image data REFID. The reference imagedata may for example comprise image data of examples of seeds thatexhibit a particular condition, e.g. being inbred, hybrid, and areference classification has been stored for each of the reference imagedata. The decision support system compares the obtained image data withthe reference image data (e.g. compares with each reference image data)and establishes which one of the reference image data has most in commonwith the image data (for example by applying a pattern recognitionalgorithm or by any other suitable comparison). The seed may then beassigned a classification based on the comparison. The assigning theclassification may either be performed by assigning the classificationof the reference image data that is most similar, or by assigning anaverage or weighted average of two or more the reference image data,i.e. reference image data from two or more seeds, to provide a higheraccuracy. The decision support system and data processing device may beimplemented in a form of software, which is for example executed by acomputer, a plurality of computers interconnected by a datacommunication network, or any other data processing arrangement. It isnoted that the classification may, according to an embodiment of theinvention, be performed by a human operator. The human operator mayperform the classification directly from the image, i.e. without adecision support system, or may be assisted by a classification providedby the decision support system.

The reference image data, being e.g. obtained from scanning referenceexamples of seeds. Thereby, the apparatus may easily be learned fordifferent seed types and different conditions, by measurement ofsample(s) in various conditions, storing the obtained image data of thereference sample(s) for comparison. The reference image data mayalternatively be pre-stored or remotely accessible, for example from aremote server connected to the decision support system via the internet.

In the case of the terahertz signal source generating a pulse, thereference image pattern(s) may be reference time domain reflectionpattern(s). Different reference time domain reflection pattern(s) may beprovided representing various genomic conditions of the seed. In thecase of a single pixel image, the reference image data may comprise areference value. Different reference values may represent variousgenomic conditions.

It is remarked that the image data may also be derived at a plurality offrequencies in the THz frequency range. For example, FIG. 5A depicts afrequency diagram showing a THz signal generator frequency sweep fromfrequency f1 to frequency f2 in the THz frequency range. A response asdetected by the detector, e.g. a reflection, is depicted in FIG. 5Bshowing a frequency diagram of a reflected signal as detected by thedetector, forming an example of a spectral signal SPS. Peaks and dips(or generally a response at a certain frequency) in the frequencyresponse curve as detected may represent specific substances such asspecific amino acids. Depending on a type of seed, the classification ofthe seed into hybrid or inbred may associate to a content of one or morespecific amino acids, such as isoleucine, glutamic acid, leucine,glycine, tyrosine, histidine and their combinations. Each of these aminoacids may exhibit a specific interaction with the THz signal, e.g. anabsorption at specific frequencies, etc. Such interaction translatesinto a specific feature in the image data, such as an absorption atspecific frequencies, or other feature. Thereby, a discriminativegenotypic feature may be derived by the decision support system and theclassification be performed with a high reliability based on theoccurrence of such specific feature in the image data. FIG. 5C depictsan example of a measurement of phase information, whereby a phase PH(0-360 degrees) of the detected signal is depicted, the phase generallybeing formed by a phase difference as compared with the source signal ora delayed source signal.

The decision support system may be learned, an example being provided asfollows: First, a set of seeds are tested in order to provide anestimate of their classification, this may be done using anothertechnique, such as NIR. Each seed of the set is then assigned aclassification (based on the analysis by the other technique). The seedsare subjected to the terahertz testing as described in order to obtainimage data for each seed of the set. The obtained image data for eachseed is coupled to the classification as obtained by the othertechnique. The image data in combination with the classification is thenstored as reference image data. Another example of learning the decisionsupport system in a learning mode LM may be to using the terahertzapparatus and/or method as described in this document for generation ofimage data for each seed of the set (step 600). Based on the image data,the classification is however performed by an operator, such as atrained operator. Alternatively, the image data may be obtained inanother way, e.g. using another classification technique. Thus, ingeneral terms, classification data is obtained and entered (step 610).The obtained image data for each seed is associated with (step 620) toclassification, such as provided by the operator or obtained fromanother classification technique. The image data in combination with theclassification is then stored as reference image data. In normaloperating mode, the reference image data may be used by the decisionsupport system e.g. for comparison of an obtained image to the referenceimages. Also, a criterion may be derived (step 630) from the storedimage data and associated classification, so that in the normaloperating mode (i.e. once the learning has been stopped), theclassification may be performed using the criterion allowing to morequickly classify the seeds using the criterion. The criterion may forexample comprise an absorption and/or a reflection in a specificfrequency band.

Another embodiment for learning patterns from THz images, comprisesusing supervised machine leaning approach, where feature vectors basedon fft (fast fourier transform) or wavelet coefficients are constructedand trained using a machine learning algorithm, e.g. such as SVM(support vector machine). Pattern recognition techniques may be used toautomatically or semi-automatically inspect THz images. The patternrecognition techniques comprises several steps. First, a “corpus”, i.e.collection of labeled examples (feature vectors) derived from THzimages, is constructed. Second, the corpus is randomly split into trainand test sets (using e.g. a 90/10 split) where the train set will beused to train the classifier and the test set will be used to evaluatethe classifier performance. Mathematically spoken, during the trainingphase a classifier learns a separation hyperplane in feature space. As ameasure of classifier performance a (classical) micro-averaged Recall,Precision and F1-value are estimated. Within these training, testing andevaluating phases the classifier is actually built. Finally, theobtained classifier is used to predict the labels (classes) for unseenexamples. As a classification algorithm we use the Support VectorMachine (SVM). SVM is a popular classification algorithm that has beenused successfully in various applications. SVM was designed to find aunique, optimal separation hyperplane. A hyperplane is consideredoptimal when it separates the positive and the negative trainingexamples in such a way that it has the largest possible margin to thenearest training examples as presented. SVM basically solves a specialconvex Quadratic Programming problem, which is quite computationallydemanding, however, an accurate estimation may be achieved.

In the exemplary example of source and detector, as will be describedbelow with reference to FIGS. 2 and 3, use is made of a vector networkanalyzer. Vector network analyzers (VNA) are known tools in microwaveand millimeter wave laboratories. They are capable of measuringamplitude response and phase response of a circuit under test, forinvestigating RF properties thereof. As will be explained below, aneffective frequency range of the VNA has been extended into the THzrange.

A quasi optics measurement scheme is described with reference to FIG. 2.A reflectometer to measure the seed under test is made by using theMichelson interferometer scheme as shown in FIG. 2. A source SRC emitsvia a horn and a HDP (high density polyethylene) lens (acting ascoupling device) the terahertz radiation towards a beam splitter, inthis example a 40 microns Mylar positioned at an angle of 45 degrees inrespect of a propagation direction of the emitted terahertz signal beam.Main polarization of set-up is vertical and is set by a polarization ofdetector and transmitter diagonal horns. A x6 multiplier is used as partof the signal source. The source has an additional WR-8 couplingwaveguide port which allows to pick part of the signal before the x6multiplier to create a reference for the phase/amplitude detectioncircuit, as will be explained below with reference to FIG. 3. Asub-harmonically pumped (n=30 . . . 35) superlattice electronic device(SLED) is used for detection. It is mounted into a detector block withintegrated diagonal horn. Its SMA type connector DC/IF input was alsoused to provide a sub harmonic LO signal at 16 . . . 20 GHz. The seed islocated in one of the arms of Michelson interferometer there as signalcoming to the other arm is absorbed by special load design to absorb THzradiation. The beam as emitted by the source and coupling device travelsto the beam splitter, where it is split into a measurement beamtravelling to the seed, and parasitic beam which is then absorbed by thebeam dump load. A beam dump load BDL absorbs a parasitic signal. Boththe reference beam and the measurement beam (as reflected by the seed),reach the beam splitter again, and reflects towards the detector DET. Achange in reflectivity changes an amplitude of the beam received by thedetector, while a change in reflectivity depth or dielectric propertiesof the seed changes a phase of the beam received by the detector.

A block schematic diagram of a source and detection circuit is depictedin FIG. 3. The source is provided with a first frequency synthesizer S1(forming an example of a microwave signal generator) in a range of 16-18GHz (forming an example of a microwave signal), which is multiplied by6, an output signal thereof being provided to mixer M1 as well as to asecond multiplier which again multiplies by 6 to generate the sourcesignal. The multipliers form an example of an up converter. Mixer M1further received a signal from a second frequency synthesizer S2 whichused both for pumping a detector SLED as well as by Schottky mixer M1for creating a reference system. The primary IF (intermediate frequency)may hence for example be 1 GHz. The IF signal of mixer M1 is amplifiedand multiplied by 6 to create a primary reference signal. The detectedsignal is mixed by the signal from synthesizer S2 to 1 GHz, the mixingforming an example of a down converter that converts to a microwavedetection signal. The primary reference signal is compared with thedetected signal taking into account the phase and amplitude informationthus providing the detector output signal. From this comparison theinformation to build the THz image data is obtained. An additional mixerpair M3, M4 was used to take out coherent phase noise introduced bysynthesizers S1 and S2 and allow for using extremely narrow detectionbandwidth of 100 Hz. A microwave VNA in time sweep mode may be used assignal detection unit. The internal VNA reference oscillator may be usedas S3. All S1, S2 and S3 are phase locked to each other. Duringmeasurements, for each point of signal frequency the oscillators S1 andS2 have been tuned such that the primary IF stays 1 GHz; output power ofS2 is adjusted to maximize S/N at SLED detector and a time sweep of VNAis taken. This procedure is repeated for each frequency, for examplefollowing a table lookup procedure in a control computer of thedetector.

The image data for a seed is built from the detector output signal incombination with position information derived from the scanning (e.g.position data communicated between the scanner and the data processingdevice). The classification is then performed as described above. FIG. 4depicts a seed selection system in accordance with an embodiment of theinvention. A feeding device FD, such as a conveyor or any other feedingdevice, provides seeds in a sequential way, one by one, to theclassification system ES, such as a classification system describedabove with reference to FIGS. 1-3. The classification system provides aseed classification output signal SGAO which provides an estimation ofthe classification of the respective seed. This signal is provided to acontrol input CI of a selector SEL (comprising e.g. an actuator todirect the seed to a corresponding output of the selector), the selectoraccordingly directs the seed to one of a plurality of its outputs SOP1,SOP2, so as to separate seeds having different classificationsaccordingly.

One implementation of a Tetarhertz time domain spectrometer is describedin the FIG. 7. A source SRC emitting a train of short optical or nearinfrared pulses (from 1 ns to 0.1 ps duration) depicted as “pulsedlaser” emits towards an optical signal splitter SPL. After splitting bythe splitter, the pulses excite a THz transmitter TTR, typicallyphotoconductive element, which produces a pulse of electromagnetic fieldproportional to a time envelope of the optical pulse and thus being inthe THz range. The emitted THz pulse is then coupled using focusingoptics to a device under test (DUT) in the present case a seed SD. THzsignal passes through the seed and is then focused onto a THz detectorDET, typically a photoconductive element. At the same time, the splittedpart of the same pulse is passed through a tunable optical delay lineDEL with delays ranging from 0 to several nanoseconds and then iscoupled to the THz detector also. When the THz pulse via the delay lineand the optical pulse from the seed arrive at the detectorsynchronously, the detector output signal will change. Typically asynchronous detection technique with pulse repetition signal as areference is used in the signal processing unit SPU. The signalprocessing unit may comprise e.g. amplifier(s), lock-in amplifier(s) andan analogue to digital converter (ADC) to convert to a digital signal tothe computer. Measuring a detector response when scanning the delay lineallows to obtain THz signal amplitude vs. time which after mathematicaltransformation in a data processing device DPD, such as a computer, thetransformation e.g. comprising a Fourier transform, provides a spectralimage SPI of the seed. THz signal source and detector may or may nothave a DC bias supplied to them. Due to symmetry, the delay line can beinstalled between the splitter and the THz detector or between thesplitter and the THz transmitter. The described configuration can bearranged as THz reflectometer.

FIG. 8 depicts a block schematic view of a decision support system DSS.Image data ID (such as the above spectral image SPI) is compared toreference image data REFID as described above. The classification CLA isderived from the comparison as described above.

The invention may for example be used in agriculture, i.e. to selectseeds in accordance with their classification into one of the classesinbred and hybrid, in order to use them for agricultural purpose, aswell as many other applications.

The invention claimed is:
 1. An apparatus for classifying a seed asinbred or hybrid, comprising: a terahertz signal source for generating aterahertz signal, a support for holding the seed, a detector fordetecting at least part of the terahertz signal having interacted withthe seed, the detector comprising a detector output and being arrangedfor generating a detector output signal at the detector output based onthe detected at least part of the terahertz signal, a data processingdevice for forming an image data from the detector output signal, and adecision support system for providing from the image data aclassification of the seed as inbred or hybrid, wherein the terahertzsignal is in a range of 0.01 to 10 THz.
 2. The apparatus according toclaim 1, wherein the detector is arranged for detecting an amplitude ofthe terahertz signal having interacted with the seed, the detectoroutput signal being representative of a detected amplitude of theterahertz signal.
 3. The apparatus according to claim 1, wherein thedetector is arranged for detecting an amplitude and a phase of theterahertz signal having interacted with the seed, the detector outputsignal being representative of a detected amplitude and phase of theterahertz signal.
 4. The apparatus according to claim 3, wherein thedata processing device is arranged for combining amplitude and phasedata as comprised in the detector output signal and for forming an imagedata of the seed therefrom.
 5. The apparatus according to claim 1,wherein the terahertz signal source is configured for generating theterahertz signal at a plurality of frequencies, the detector beingconfigured to detect at least part of the terahertz signal havinginteracted with the seed at each of the plurality of frequencies, thedetector output signal comprising a spectral signal, the data processingdevice being configured for forming a spectral image from the detectoroutput signal.
 6. The apparatus according to claim 1, wherein theterahertz signal source comprises a microwave signal generator forgenerating a microwave signal and an up-converter, connected to anoutput of the microwave signal generator, for converting the microwavesignal into the a terahertz frequency band.
 7. The apparatus accordingto claim 1, wherein the detector comprises a down-converter forconverting the detected at least part of the terahertz signal havinginteracted with the seed into a microwave detection signal.
 8. Theapparatus according to claim 1, wherein the signal source and thedetector are arranged for free space coupling with the seed.
 9. Theapparatus according to claim 1, wherein the decision support system isarranged for comparing the obtained image data of the seed with at leastone reference image data stored by the decision support system, andderiving from the comparison the classification of the seed as inbred orhybrid.
 10. The apparatus according to claim 1, further comprising ascanner for moving the support relative to the terahertz signal toprovide a scan of the seed, wherein the data processing device isarranged for forming the image data from the detector output signal asobtained for a plurality of positions during the scan of the seed. 11.The apparatus according to claim 1, wherein the apparatus is furtherconfigured to operate in a learning mode, the decision support system inthe learning mode being configured to store image data and spectralimages as obtained from seeds of a set of learning seeds, and toassociate a given classification to the image data and spectral imagesas obtained from the seeds of the set of learning seeds.
 12. Theapparatus according to claim 11, wherein the decision support system isfurther configured to in the learning mode derive a criterion forclassification of a seed from the image data of the set of learningseeds and the associated given classification data, and to provide theclassification of a following seed not comprised in the set of learningseeds, using the criterion.
 13. The apparatus according to claim 12,wherein the criterion comprises at least one of an absorption in aspecified frequency band, a reflection in a specified frequency band.14. A method for classifying a seed as inbred or hybrid, comprising:generating a terahertz signal, holding the seed by a support, couplingthe terahertz signal to the seed, detecting at least part of theterahertz signal having interacted with the seed and generating adetector output signal based on the detected at least part of theterahertz signal, forming an image data from the detector output signal,and, providing from the image data a classification of the seed asinbred or hybrid, wherein the terahertz signal is in a range of 0.01 to10 THz.
 15. The method according to claim 14, wherein detecting at leastpart of the terahertz signal having interacted with the seed comprisesdetecting an amplitude of the terahertz signal having interacted withthe seed, the detector output signal being representative of a detectedamplitude of the terahertz signal.
 16. The method according to claim 14,wherein detecting at least part of the terahertz signal havinginteracted with the seed comprises detecting an amplitude and a phase ofthe terahertz signal having interacted with the seed, the detectoroutput signal being representative of a detected amplitude and phase ofthe terahertz signal.
 17. The method according to claim 16, wherein theforming the image data from the detector output signal comprisescombining amplitude and phase data as comprised in the detector outputsignal and forming the image data of the seed from the combinedamplitude and phase data.
 18. The method according to claim 14,comprising generating the terahertz signal at a plurality offrequencies, detecting at least part of the terahertz signal havinginteracted with the seed at each of the plurality of frequencies therebyproviding a spectral signal, and forming a spectral image from thespectral signal.
 19. The method according to claim 14, wherein thegenerating the terahertz signal comprises generating a microwave signaland up-converting the microwave signal into a terahertz frequency band.20. The method according to claim 14, wherein the detecting at leastpart of the terahertz signal having interacted with the seed comprisesdownconverting the detected at least part of the terahertz signal havinginteracted with the seed into a microwave detection signal.
 21. Themethod according to claim 14, wherein the coupling the terahertz signalto the seed and the detecting are performed by free space coupling withthe seed.
 22. The method according to claim 14, wherein the providingthe estimate of the hybrid/inbreed features from the image datacomprises: comparing the obtained image data of the seed with at leastone reference image data stored by the decision support system, andderiving from the comparison the classification of the seed as inbred orhybrid.
 23. The method according to any of claim 14, further comprising:moving the support relative to the terahertz signal to provide a scan ofthe seed, wherein forming an image data from the detector output signalcomprises forming the image data from the detector output signal asobtained for a plurality of positions during the scan of the seed. 24.The method according to claim 14, comprising operating in a learningmode, in the learning mode storing image data as obtained from seeds ofa set of learning seeds, and associating a given classification to theimage data as obtained from the seeds of the set of learning seeds. 25.The method according to claim 24, comprising in the learning modederiving a criterion for classification of a seed from the image data ofthe set of learning seeds and the associated given classification data,and providing the classification of a following seed not comprised inthe set of learning seeds, using the criterion.
 26. The method accordingto claim 25, wherein the criterion comprises at least one of anabsorption in a specified frequency band, a reflection in a specifiedfrequency band.
 27. A selection system for selecting a seed, comprising:an apparatus according to claim 1, the apparatus further comprising aseed classification output and being arranged for providing a seedclassification output signal at the seed classification output, the seedclassification output signal being representative of a classification ofthe seed as inbred or hybrid, a feeder, upstream of the apparatus, forfeeding a seed into the apparatus, a separator, downstream of theapparatus, the separator having a control input being connected to theseed classification output of the apparatus, the separator beingarranged for directing the seed to a first output of the separator inresponse to the seed classification output signal having a first valueand to a second output of the separator in response to the seedclassification output signal having a second value.
 28. The apparatusaccording to claim 1, wherein the terahertz signal source is arrangedfor providing a synchronization signal to the detector, or vice versa,in order for the detector to operate in synchronism with the terahertzsignal source allowing to perform a phase measurement by the detector.29. The apparatus according to claim 1, wherein the generated terahertzsignal is a continuous wave terahertz signal.
 30. The apparatusaccording to claim 1, wherein the image data comprises multiple pixelsassociated with the respective detector output signals.